A Survey on Artificial Intelligence in Cancer Medical and Nonmedical Datasets

A Survey on Artificial Intelligence in Cancer Medical and Nonmedical Datasets

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : Ajni K Ajai, A. Anitha
DOI :  10.14445/22315381/IJETT-V69I3P231

Citation 

MLA Style: Ajni K Ajai, A. Anitha"A Survey on Artificial Intelligence in Cancer Medical and Nonmedical Datasets" International Journal of Engineering Trends and Technology 69.3(2021):201-210. 

APA Style:Ajni K Ajai, A. Anitha. A Survey on Artificial Intelligence in Cancer Medical and Nonmedical Datasets  International Journal of Engineering Trends and Technology, 69(3),201-210.

Abstract
Advances are the outcome of continually building on previous findings and surveillances. The study of cancer intends to a day when all cancers are cured by expanding efficient methods to prevent, detect, diagnose, treat cancer. This survey can accumulate extensive knowledge about solving meaningful, challenging, and neglected problems in cancer research. When the prognosis is worse and the treatment options are more critical, it leads the patients to the late stage of the disease, but if cancer diagnoses early, survival will be significantly improved. AI is changing our lives, and its work is detonating biomedical research and health care. The application potentials of AI are huge in all levels of cancer research. The integration of AI technology into cancer care is about saving a life through image analyzing, improving accuracy, speeding up the diagnosis, aid clinical decision-making, and patient triage with debility to reduce variation and duplicate testing. The subsets of AI are machine learning and deep learning. This review, pointing to the experimentally proved problem-solving for some challenging issues through their most effective methods.

Reference
[1] R. Agarwal, 15 Examples of Artificial Intelligence You’re Using in Daily Life, Beebom publishing, https://beebom.com/examples-of-artificial-intelligence/.,( 2020).
[2] Stefan van Duin and Naser Bakhshi, Artificial Intelligence: Key characteristics of an AI, Deloitte publishing,(2018). https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/deloitte-analytics/deloitte-nl-data-analytics-artificial-intelligence-whitepaper-eng.pdf.
[3] R. Reynoso, 4 Main Types of AI, Learning hub, (2019). https://learn.g2.com/types-of-artificial-intelligence.
[4] C. Aguis, Evolution of AI: Past, Present, Future, Data-Driven Investor, (2019). https://medium.com/datadriveninvestor/evolutionof-ai-past-present-future-6f995d5f964a .
[5] V. Singh, N. K. Verma, and Y. Cui, Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics, IEEE Trans. Nanobioscience, 18(3)(2019) 482–489.
[6] B. Xu et al., Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification, IEEE Trans. Med. Imaging, 39(6)(2020) 1930–1941.
[7] O. Ozdemir, R. L. Russell, and A. A. Berlin, A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans, IEEE Trans. Med. Imaging, 39(5)(2020) 1419–1429.
[8] C. Wang et al., A Cancer Survival Prediction Method Based on Graph Convolutional Network, IEEE Trans. Nanobioscience, 19(1)(2020) 117–126.
[9] S. Hussein, P. Kandel, C. W. Bolan, M. B. Wallace, and U. Bagci., Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches, IEEE Trans. Med. Imaging,38(8) (2019) 1777–1787.
[10] J. Ma et al., Association Rule-Based Breast Cancer Prevention and Control System, IEEE Trans. Comput. Soc. Syst., 6(5) (2019) 1106–1114.
[11] G. Sharma, P. S. Rana, and S. Bawa., Hybrid machine learning models for predicting types of Human T-cell Lymphotropic Virus, IEEE/ACM Trans. Comput. Biol. Bioinforma.,(2019) 1–1.
[12] T. Feng, J. I. Davila, Y. Liu, S. Lin, S. Huang, and C. Wang., Semi-supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets, IEEE/ACM Trans. Comput. Biol. Bioinforma., (2019) 1–1.
[13] X. Zhang et al., Classification of Whole Mammogram and Tomosynthesis Images Using Deep Convolutional Neural Networks, IEEE Trans. Nanobioscience, 17(3)(2018) 237–242,
[14] C. T. Sari and C. Gunduz-Demir., Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images, IEEE Trans. Med. Imaging, 38(5) (2019) 1139–1149.
[15] D. Sun, M. Wang, and A. Li, A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data, IEEE/ACM Trans. Comput. Biol. Bioinforma., 16(3)(2019) 841–850.
[16] H.-C. Cheng et al., Deep-Learning-Assisted Volume Visualization, IEEE Trans. Vis. Comput. Graph., 25(2)(2019) 1378–1391.
[17] Y. Xie et al., Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT, IEEE Trans. Med. Imaging, 38(4)(2019) 991–1004.
[18] J. Zhang, A. Saha, Z. Zhu, and M. A. Mazurowski., Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics, IEEE Trans. Med. Imaging, 38(2)(2019) 435–447.
[19] M. Saha and C. Chakraborty., Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation, IEEE Trans. Image Process., 27(5)(2018) 2189–2200.
[20] S. Y. Shin, S. Lee, I. D. Yun, S. M. Kim, and K. M. Lee., Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images, IEEE Trans. Med. Imaging, 38(3)(2019) 762–774.
[21] B. Fu, P. Liu, J. Lin, L. Deng, K. Hu, and H. Zheng., Predicting Invasive Disease-Free Survival for Early Stage Breast Cancer Patients Using Follow-Up Clinical Data, IEEE Trans. Biomed. Eng., 66(7)(2019) 2053–2064.
[22] H.-C. Wu, X.-G. Wei, and S.-C. Chan., Novel Consensus Gene Selection Criteria for Distributed GPU Partial Least Squares-Based Gene Microarray Analysis in Diffused Large B Cell Lymphoma (DLBCL) and Related Findings, IEEE/ACM Trans. Comput. Biol. Bioinforma., 156 (2018) 2039–2052.
[23] A. Masood et al., Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT, IEEE J. Transl. Eng. Heal. Med., 8 (2019) 1-1.
[24] C. B. Alkan and Z. Isik., Characterization of Cancer Types by Applying Machine Learning Methods on Blood RNA-Sequencing Data, in 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), (2019) 1–4.
[25] A. Jalalian and B. Karasfi., Machine Learning Techniques for Challenging Tumor Detection and Classification in Breast Cancer, in 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium, (2018) 19–24.
[26] L. Cai, T. Long, Y. Dai, and Y. Huang., Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis, IEEE Access, 8 (2020) 44400–44409.
[27] Q. Zhou, B. Yong, Q. Lv, J. Shen, and X. Wang., Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection, IEEE Access, 8 (2020) 45156–45166.
[28] L. Cai, T. Long, Y. Dai, and Y. Huang., Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis, IEEE Access, 8 (2020) 44400–44409.
[29] Y. Han et al., Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology, IEEE Access, 8 (2020) 44924–44935.
[30] https://hackernoon.com/hn-images/1*RJ7KdSh-3wQvNy_hQZApzA.png
[31] https://www.edureka.co/blog/types-of-artificial-intelligence/
[32] https://www.youtube.com/watch?v=X-FKcenZ-jo
[33] https://en.proft.me/media/science/ml_types2.png
[34] http://3.bp.blogspot.com/-X81SfTlAm5Y/T4y7VyIo7cI/AAAAAAAAAIA/sLAzr5qYNYs/s1600/biological_neuron_vs_ANN.png
[35] https://cdn-images-1.medium.com/max/1600/1*dnvGC-PORSoCo7VXT3PV_A.png
[36] https://www.statista.com/statistics/938794/global-healthcare-artificial-intelligence-market-growth-rate/

Keywords
Clinical information, Deep learning, Early detection, Image analysis, Machine learning, Prevention.